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underwater target Detection Based on Reinforcement Learning and Ant Colony Optimization

Journal of Ocean University of China 2022年第2期21卷 323-330页

作者： WANG Xinhua ZHU Yungang LI Dayu ZHANG Guang School of Computer Science Northeast Electric Power UniversityJilin 132012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchun 130012China State Key Laboratory of Applied Optics Changchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China

underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target detection.However,low-light and high-noise scenarios pose great challenges in un-derwater image and video analyses.To improve the accuracy and anti-noise performance of underwater target image edge detection,an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this paper.First,the reinforcement learning concept is integrated into artificial ants’movements,and a variable radius sensing strategy is pro-posed to calculate the transition probability of each pixel.These methods aim to avoid undetection and misdetection of some pixels in image edges.Second,a double-population ant colony strategy is proposed,where the search process takes into account global search and local search abilities.Experimental results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance.

关键词： ant colony optimization reinforcement learning underwater target edge detection

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underwater target Bearing Only Tracking Based on First Order Divided Difference Filter 25th

Underwater Target Bearing Only Tracking Based on First Order...

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第25届中国控制与决策会议

作者： Xu Jinlong Wang Hongjian Zhang Aihua College of Automation Harbin Engineering University

ISBN: (纸本)9781467355339

The passive sonar based target tracking is of great importance in practical applications. In underwater bearings only target tracking research, the traditional extended kalman filtering method uses Tarlor expansion to achieve the system nonlinear terms linearization, and it leads to a complicated computing time and singularity problems. This paper presents a method based on the First Order Divided Differential Filter （DDF1） algorithm for solving the problem. The DDF1 is derived based on polynomial approximations obtained with multidimensional Stirling interpolation formula, the implementation is significantly simpler because of no derivatives is required. The results of simulation and consistency test showed that the proposed algorithm has high tracking precision and tracking performance compared with the traditional EKF method under the same simulation conditions.

关键词： underwater target Bearing only tracking First order divided difference filter （DDF1） Extended kalman filter （EKF） and Consistency testing

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Bark-Wavelet Analysis and Hilbert -Huang Transform for underwater target Recognition

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Defence Technology（防务技术） 2013年第2期9卷 145-151页

作者： ZENG Xiangyang WANG Shuguang College of Marine Northwestern Polytechnical University

Recognizing the underwater targets by the radiated noise information is one of the most significant subjects in the area of underwater acoustics. Based on the theory of auditory perception, a novel recognition approach which consists of the algorithms of Bark-wavelet analysis, Hilbert-Huang transform and support vector machine is proposed. The performance of the proposed method is validated by comparing with traditional method and evaluated by the recognition experiments for SNRs of 0 dB, 5 dB, 10 dB, 15 dB and 20 dB.The results show that the average recognition rate of the method is above 88% and can be increased by 0.75 % to 6.25% under various SNR conditions compared to the baseline system.

关键词： acoustics underwater target recognition Bark-wavelet Hilbert-Huang transform

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underwater target Threat Assessment Method Based on Bayesian Network

Underwater Target Threat Assessment Method Based on Bayesian...

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第40届中国控制会议

作者： Danyi Li Meiqin Liu Senlin Zhang State Key Laboratory of Industry Control Technology Zhejiang University College of Electrical Engineering Zhejiang University

underwater targets are used more and more frequently in modern ocean battlefield.It is important to assess the threat from an enemy target after it is detected immediately,in order to prepare quick counterattack and defense.This paper proposes a method for underwater target threat assessment based on Bayesian Network.The threat model uses a hierarchy view for threat factors.All factors are divided into three parts:environment,target space and target fire.Each part contains several detailed factors,which are arranged based on their causal relationships.These factors are applied into the Bayesian Network and the probability of threat is calculated through Bayesian inference.Finally,a simulation is conducted and the method can effectively assess the target threat.The model has a relatively good performance and can generate the assessment with low time cost.

关键词： underwater target threat assessment situation awareness Bayesian Network Belief Propagation Algorithm

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Distributed target Tracking using Consensus-based Bayesian Filtering by underwater Acoustic Sensor Networks

Distributed Target Tracking using Consensus-based Bayesian F...

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第36届中国控制会议

作者： Jing Yan Bin Pu Xuyang Tian Xiaoyuan Luo Xinbin Li Xinping Guan Institute of Electrical Engineering Yanshan University Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

ISBN: (纸本)9781538629185

underwater target tracking,which aims to achieve accurate position of underwater target,has been one of the key issues in the underwater research.However,due to the unique underwater environment such as extraneous interference and weak communication,how to realize the accurate tracking of underwater target becomes a challenging problem.In this paper,we are concerned with the problem of underwater target tracking.The Bayesian based rule is proposed to judge the existence of target for single sensor node.In order to realize the tracking for the underwater target,a consensus-based Bayesian tracking method is proposed,which possesses better accuracy and spends less time in communication.In addition,the consensus of the consensus-based Bayesian algorithm is analyzed.Finally,simulation results are performed to show the effectiveness of the proposed algorithm in this paper.

关键词： underwater target tracking acoustic sensor networks consensus

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Rough set-based feature selection method

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Progress in Natural Science:Materials International 2005年第3期 88-92页

作者： ZHAN Yanmei,ZENG Xiangyang ˇ and SUN Jincai (College of Marine Engieering,Northwestern Polytechnical University,Xi’an710072,China)

A new feature selection method is proposed based on the discern matrix in rough set in this paper.The main idea of this method is that the most effective feature,if used for classification,can distinguish the most number of samples belonging to different class-es.Experiments are performed using this method to select relevant features for artificial datasets and real-world datasets.Results show that the selection method proposed can correctly select all the relevant features of artificial datasets and drastically reduce the number of fea-tures at the same time.In addition,when this method is used for the selection of classification features of real-world underwater targets,the number of classification features after selection drops to20%of the original feature set,and the classification accuracy increases about6%using dataset after feature selection.

关键词： underwater target rough set discern matrix feature selection.

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